Scalable unsupervised labeling with SHAP feature selection for fraud detection in imbalanced data
Abstract There is a growing need for labeled data, yet manual annotation is costly, error-prone, and often infeasible in privacy-sensitive, highly imbalanced domains such as fraud detection. We introduce a fully unsupervised framework that combines unsupervised SHapley Additive exPlanations (SHAP) f...
Tallennettuna:
| Päätekijät: | , |
|---|---|
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
SpringerOpen
2025-10-01
|
| Sarja: | Journal of Big Data |
| Aiheet: | |
| Linkit: | https://doi.org/10.1186/s40537-025-01248-w |
| Tagit: |
Ei tageja, Lisää ensimmäinen tagi!
|